Predicting functional outcomes of patients with spontaneous intracerebral hemorrhage based on explainable machine learning models: a multicenter retrospective study
The XGB model developed in this study can effectively predict the risk of poor prognosis in patients with SICH, helping clinicians make personalized and rational clinical decisions. Prognostic risk in patients with SICH is closely associated with GCS score, hematoma volume, blood pressure changes, platelets, age, bleeding location, and blood glucose levels.
Authors
- Fengda Li
- Chuanghong Liu
- Longyuan Gu
- Kaijian Xia (ORCID: https://orcid.org/0000-0002-1650-9982)
- Chengfa Sun
- Hong Xu (ORCID: https://orcid.org/0000-0002-9767-1761)
- Kai-Yuan Cheng
- Zeyi Li (ORCID: https://orcid.org/0009-0006-5990-1825)
- Gang Kong (ORCID: https://orcid.org/0000-0002-4056-4797)
- Bin Pan (ORCID: https://orcid.org/0000-0003-4872-1221)
Institutions
- Soochow University (CN)
- Nanjing University of Posts and Telecommunications (CN)
- Changshu No.1 People's Hospital (CN)
Publication Details
- Journal
- Frontiers in Neurology
- Published
- 2025-01-10
- DOI
- https://doi.org/10.3389/fneur.2024.1494934
- Citations
- 2
- Primary Topic
- Intracerebral and Subarachnoid Hemorrhage Research
- Type
- article
- Field-Weighted Citation Impact
- 2.78